1. Field of the Invention
The invention is related to the field of atmospheric turbulence, and in particular, to systems and methods to detect and display atmospheric turbulence.
2. Statement of the Problem
The use of Global Position System (GPS) satellite signals for location determination is well known. Environmental conditions affect GPS signals, and thus, GPS signals have also been processed to monitor environmental conditions. For example, GPS signals have been processed to monitor temperature, wind, and water vapor in the atmosphere. Changes in the index of refraction or refractivity as the GPS signal passes through the atmosphere are a function of the temperature and water vapor content along the GPS signal path. Most meteorological applications of GPS technology seek to separate the temperature and water vapor effects. Some attempts have been made to process GPS signals to assess atmospheric turbulence. As discussed below, these attempts have failed to produce an effective system to process GPS signals to produce a three-dimensional map of atmospheric turbulence.
One technique for processing GPS signal to monitor atmospheric turbulence is described in Characterizing Atmospheric Turbulence with GPS by F. Kleijer, P. Elosegue, and J. L. Davis (1994). This technique postulates that the tropospheric delay caused by turbulence to be the zero-mean difference between the actual slant path delay and a mapped zenith delay. The turbulence strength factor obtained from a single GPS receiver site at Mount Washington, N.H. shows temporal variations that could be related to turbulence strength. However, there is no indication of the location of the turbulence as a function of height. The fundamental concern of the technique is that the data is a measure of the index of refraction fluctuations which consist of water vapor variations and gradients of water vapor mixed in with turbulence information. Clear air turbulence, primarily related to velocity gradients and has not been identified with water vapor fluctuations. The problem with this technique is that water vapor change is mixed in with the data. There can be heterogeneous variability of water vapor without any turbulence. The technique does not mention how a final four dimensional mapping in space and time of the dynamic atmospheric turbulence field can be achieved.
Another technique for processing GPS signals to monitor atmospheric turbulence is described in The Effect of Turbulence of GPS Signals: Theory and Measurement by L. Cornman and R. Frelich (July 2004). This technique recognizes that GPS data received at the ground can be characterized by strong humidity variations in the boundary layer, making it difficult to separate out fluctuations due to humidity and temperature fluctuations. This technique chooses to use GPS-LEO occultation data (data gathered in a vertical column) to get vertical information about atmospheric turbulent structures above the boundary layer. The technique uses GPS amplitude and phase data, removes the mean atmospheric effects (e.g., from a high resolution atmospheric model), then performs a Fourier spectrum analysis of selected time intervals of the occultation profile to get amplitude and phase spectra. The technique seems to produce reasonable agreement between the measured and modeled spectra. The technique deliberately sets out to use fluctuations of GPS signals passing through the upper regions of the atmosphere that are mainly influenced by temperature-induced turbulence—not water vapor. The limitation of the technique is that the relevant equation has three unknowns: L0 the length scale of the turbulence, C2 the turbulence intensity, and Δη the thickness of the vertical patch of turbulence. The length scale was assumed to be 3000 m a priori, and the combined two variables [C2Δη] are found by a single variable maximum likelihood fit. The problem is that Δη can vary greatly (and with a single occultation Δη cannot be determined) so that the intensity cannot be known for sure. The technique suggests that if many occultations (vertical profiles) through the same region of space were available, tomographic methods could be used. One could add occulatations (vertical profiles) from commercial aircraft with GPS receivers. However, the lower aircraft occultations would be influenced by water vapor—the very problem the statistical approximations and scattering theory used in the approach were trying to avoid. A far bigger problem is that the number of occultations per unit area would not be sufficient for convergence and removal of the ambiguity between the three unknowns listed above.
Another technique, described in U.S. Pat. No. 6,738,010, looked at signals transmitted from aircraft near the airport to assess turbulence near the airport. This technique measured phase change of the transmitted signals helped to identify turbulence. This technique did not use GPS signals or any other satellite signals from a system that is already deployed.